230 lines
8.2 KiB
Plaintext
230 lines
8.2 KiB
Plaintext
|
Metadata-Version: 2.1
|
||
|
|
Name: cymem
|
||
|
|
Version: 2.0.8
|
||
|
|
Summary: Manage calls to calloc/free through Cython
|
||
|
|
Home-page: https://github.com/explosion/cymem
|
||
|
|
Author: Matthew Honnibal
|
||
|
|
Author-email: matt@explosion.ai
|
||
|
|
License: MIT
|
||
|
|
Classifier: Environment :: Console
|
||
|
|
Classifier: Intended Audience :: Developers
|
||
|
|
Classifier: Intended Audience :: Science/Research
|
||
|
|
Classifier: License :: OSI Approved :: MIT License
|
||
|
|
Classifier: Operating System :: POSIX :: Linux
|
||
|
|
Classifier: Operating System :: MacOS :: MacOS X
|
||
|
|
Classifier: Operating System :: Microsoft :: Windows
|
||
|
|
Classifier: Programming Language :: Cython
|
||
|
|
Classifier: Programming Language :: Python :: 2.6
|
||
|
|
Classifier: Programming Language :: Python :: 2.7
|
||
|
|
Classifier: Programming Language :: Python :: 3.3
|
||
|
|
Classifier: Programming Language :: Python :: 3.4
|
||
|
|
Classifier: Programming Language :: Python :: 3.5
|
||
|
|
Classifier: Programming Language :: Python :: 3.6
|
||
|
|
Classifier: Programming Language :: Python :: 3.7
|
||
|
|
Classifier: Programming Language :: Python :: 3.8
|
||
|
|
Classifier: Programming Language :: Python :: 3.9
|
||
|
|
Classifier: Programming Language :: Python :: 3.10
|
||
|
|
Classifier: Programming Language :: Python :: 3.11
|
||
|
|
Classifier: Programming Language :: Python :: 3.12
|
||
|
|
Classifier: Topic :: Scientific/Engineering
|
||
|
|
Description-Content-Type: text/markdown
|
||
|
|
License-File: LICENSE
|
||
|
|
|
||
|
|
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
|
||
|
|
|
||
|
|
# cymem: A Cython Memory Helper
|
||
|
|
|
||
|
|
cymem provides two small memory-management helpers for Cython. They make it easy
|
||
|
|
to tie memory to a Python object's life-cycle, so that the memory is freed when
|
||
|
|
the object is garbage collected.
|
||
|
|
|
||
|
|
[](https://github.com/explosion/cymem/actions/workflows/tests.yml)
|
||
|
|
[](https://pypi.python.org/pypi/cymem)
|
||
|
|
[](https://anaconda.org/conda-forge/cymem)
|
||
|
|
[](https://github.com/explosion/wheelwright/releases)
|
||
|
|
|
||
|
|
## Overview
|
||
|
|
|
||
|
|
The most useful is `cymem.Pool`, which acts as a thin wrapper around the calloc
|
||
|
|
function:
|
||
|
|
|
||
|
|
```python
|
||
|
|
from cymem.cymem cimport Pool
|
||
|
|
cdef Pool mem = Pool()
|
||
|
|
data1 = <int*>mem.alloc(10, sizeof(int))
|
||
|
|
data2 = <float*>mem.alloc(12, sizeof(float))
|
||
|
|
```
|
||
|
|
|
||
|
|
The `Pool` object saves the memory addresses internally, and frees them when the
|
||
|
|
object is garbage collected. Typically you'll attach the `Pool` to some cdef'd
|
||
|
|
class. This is particularly handy for deeply nested structs, which have
|
||
|
|
complicated initialization functions. Just pass the `Pool` object into the
|
||
|
|
initializer, and you don't have to worry about freeing your struct at all — all
|
||
|
|
of the calls to `Pool.alloc` will be automatically freed when the `Pool`
|
||
|
|
expires.
|
||
|
|
|
||
|
|
## Installation
|
||
|
|
|
||
|
|
Installation is via [pip](https://pypi.python.org/pypi/pip), and requires
|
||
|
|
[Cython](http://cython.org). Before installing, make sure that your `pip`,
|
||
|
|
`setuptools` and `wheel` are up to date.
|
||
|
|
|
||
|
|
```bash
|
||
|
|
pip install -U pip setuptools wheel
|
||
|
|
pip install cymem
|
||
|
|
```
|
||
|
|
|
||
|
|
## Example Use Case: An array of structs
|
||
|
|
|
||
|
|
Let's say we want a sequence of sparse matrices. We need fast access, and a
|
||
|
|
Python list isn't performing well enough. So, we want a C-array or C++ vector,
|
||
|
|
which means we need the sparse matrix to be a C-level struct — it can't be a
|
||
|
|
Python class. We can write this easily enough in Cython:
|
||
|
|
|
||
|
|
```python
|
||
|
|
"""Example without Cymem
|
||
|
|
|
||
|
|
To use an array of structs, we must carefully walk the data structure when
|
||
|
|
we deallocate it.
|
||
|
|
"""
|
||
|
|
|
||
|
|
from libc.stdlib cimport calloc, free
|
||
|
|
|
||
|
|
cdef struct SparseRow:
|
||
|
|
size_t length
|
||
|
|
size_t* indices
|
||
|
|
double* values
|
||
|
|
|
||
|
|
cdef struct SparseMatrix:
|
||
|
|
size_t length
|
||
|
|
SparseRow* rows
|
||
|
|
|
||
|
|
cdef class MatrixArray:
|
||
|
|
cdef size_t length
|
||
|
|
cdef SparseMatrix** matrices
|
||
|
|
|
||
|
|
def __cinit__(self, list py_matrices):
|
||
|
|
self.length = 0
|
||
|
|
self.matrices = NULL
|
||
|
|
|
||
|
|
def __init__(self, list py_matrices):
|
||
|
|
self.length = len(py_matrices)
|
||
|
|
self.matrices = <SparseMatrix**>calloc(len(py_matrices), sizeof(SparseMatrix*))
|
||
|
|
|
||
|
|
for i, py_matrix in enumerate(py_matrices):
|
||
|
|
self.matrices[i] = sparse_matrix_init(py_matrix)
|
||
|
|
|
||
|
|
def __dealloc__(self):
|
||
|
|
for i in range(self.length):
|
||
|
|
sparse_matrix_free(self.matrices[i])
|
||
|
|
free(self.matrices)
|
||
|
|
|
||
|
|
|
||
|
|
cdef SparseMatrix* sparse_matrix_init(list py_matrix) except NULL:
|
||
|
|
sm = <SparseMatrix*>calloc(1, sizeof(SparseMatrix))
|
||
|
|
sm.length = len(py_matrix)
|
||
|
|
sm.rows = <SparseRow*>calloc(sm.length, sizeof(SparseRow))
|
||
|
|
cdef size_t i, j
|
||
|
|
cdef dict py_row
|
||
|
|
cdef size_t idx
|
||
|
|
cdef double value
|
||
|
|
for i, py_row in enumerate(py_matrix):
|
||
|
|
sm.rows[i].length = len(py_row)
|
||
|
|
sm.rows[i].indices = <size_t*>calloc(sm.rows[i].length, sizeof(size_t))
|
||
|
|
sm.rows[i].values = <double*>calloc(sm.rows[i].length, sizeof(double))
|
||
|
|
for j, (idx, value) in enumerate(py_row.items()):
|
||
|
|
sm.rows[i].indices[j] = idx
|
||
|
|
sm.rows[i].values[j] = value
|
||
|
|
return sm
|
||
|
|
|
||
|
|
|
||
|
|
cdef void* sparse_matrix_free(SparseMatrix* sm) except *:
|
||
|
|
cdef size_t i
|
||
|
|
for i in range(sm.length):
|
||
|
|
free(sm.rows[i].indices)
|
||
|
|
free(sm.rows[i].values)
|
||
|
|
free(sm.rows)
|
||
|
|
free(sm)
|
||
|
|
```
|
||
|
|
|
||
|
|
We wrap the data structure in a Python ref-counted class at as low a level as we
|
||
|
|
can, given our performance constraints. This allows us to allocate and free the
|
||
|
|
memory in the `__cinit__` and `__dealloc__` Cython special methods.
|
||
|
|
|
||
|
|
However, it's very easy to make mistakes when writing the `__dealloc__` and
|
||
|
|
`sparse_matrix_free` functions, leading to memory leaks. cymem prevents you from
|
||
|
|
writing these deallocators at all. Instead, you write as follows:
|
||
|
|
|
||
|
|
```python
|
||
|
|
"""Example with Cymem.
|
||
|
|
|
||
|
|
Memory allocation is hidden behind the Pool class, which remembers the
|
||
|
|
addresses it gives out. When the Pool object is garbage collected, all of
|
||
|
|
its addresses are freed.
|
||
|
|
|
||
|
|
We don't need to write MatrixArray.__dealloc__ or sparse_matrix_free,
|
||
|
|
eliminating a common class of bugs.
|
||
|
|
"""
|
||
|
|
from cymem.cymem cimport Pool
|
||
|
|
|
||
|
|
cdef struct SparseRow:
|
||
|
|
size_t length
|
||
|
|
size_t* indices
|
||
|
|
double* values
|
||
|
|
|
||
|
|
cdef struct SparseMatrix:
|
||
|
|
size_t length
|
||
|
|
SparseRow* rows
|
||
|
|
|
||
|
|
|
||
|
|
cdef class MatrixArray:
|
||
|
|
cdef size_t length
|
||
|
|
cdef SparseMatrix** matrices
|
||
|
|
cdef Pool mem
|
||
|
|
|
||
|
|
def __cinit__(self, list py_matrices):
|
||
|
|
self.mem = None
|
||
|
|
self.length = 0
|
||
|
|
self.matrices = NULL
|
||
|
|
|
||
|
|
def __init__(self, list py_matrices):
|
||
|
|
self.mem = Pool()
|
||
|
|
self.length = len(py_matrices)
|
||
|
|
self.matrices = <SparseMatrix**>self.mem.alloc(self.length, sizeof(SparseMatrix*))
|
||
|
|
for i, py_matrix in enumerate(py_matrices):
|
||
|
|
self.matrices[i] = sparse_matrix_init(self.mem, py_matrix)
|
||
|
|
|
||
|
|
cdef SparseMatrix* sparse_matrix_init_cymem(Pool mem, list py_matrix) except NULL:
|
||
|
|
sm = <SparseMatrix*>mem.alloc(1, sizeof(SparseMatrix))
|
||
|
|
sm.length = len(py_matrix)
|
||
|
|
sm.rows = <SparseRow*>mem.alloc(sm.length, sizeof(SparseRow))
|
||
|
|
cdef size_t i, j
|
||
|
|
cdef dict py_row
|
||
|
|
cdef size_t idx
|
||
|
|
cdef double value
|
||
|
|
for i, py_row in enumerate(py_matrix):
|
||
|
|
sm.rows[i].length = len(py_row)
|
||
|
|
sm.rows[i].indices = <size_t*>mem.alloc(sm.rows[i].length, sizeof(size_t))
|
||
|
|
sm.rows[i].values = <double*>mem.alloc(sm.rows[i].length, sizeof(double))
|
||
|
|
for j, (idx, value) in enumerate(py_row.items()):
|
||
|
|
sm.rows[i].indices[j] = idx
|
||
|
|
sm.rows[i].values[j] = value
|
||
|
|
return sm
|
||
|
|
```
|
||
|
|
|
||
|
|
All that the `Pool` class does is remember the addresses it gives out. When the
|
||
|
|
`MatrixArray` object is garbage-collected, the `Pool` object will also be
|
||
|
|
garbage collected, which triggers a call to `Pool.__dealloc__`. The `Pool` then
|
||
|
|
frees all of its addresses. This saves you from walking back over your nested
|
||
|
|
data structures to free them, eliminating a common class of errors.
|
||
|
|
|
||
|
|
## Custom Allocators
|
||
|
|
|
||
|
|
Sometimes external C libraries use private functions to allocate and free
|
||
|
|
objects, but we'd still like the laziness of the `Pool`.
|
||
|
|
|
||
|
|
```python
|
||
|
|
from cymem.cymem cimport Pool, WrapMalloc, WrapFree
|
||
|
|
cdef Pool mem = Pool(WrapMalloc(priv_malloc), WrapFree(priv_free))
|
||
|
|
```
|